1 publications with the keyword "kernelized correlation filter"

Recently our group proposed LoFT (Likelihood of Features Tracking) tracker system [1] that can successfully track objects of interest under different scenarios of wide-area motion imagery and full motion video. LoFT is a recognition-based single target tracker that relies on fusion of multiple complementary features. In this paper, LoFT is extended with a kernelized correlation filter (KCF) module to incorporate a robust continuous target template update scheme to better localize the target and to recover from sudden appearance changes and occlusions. Decision module using peak-to-sidelobe ratio is added to KCF module to prevent error accumulation from blending non-target regions to target template during update, and to prevent fusion of the KCF likelihood map to the other LoFT feature likelihood maps when the KCF response is not reliable. KC-LoFT is a single object tracker that fuses the most discriminative features from LoFT and KCF to better localize the target object in the search window. KC-LoFT was tested on ABQ aerial wide area motion imagery dataset [2] and produced promising results compared to recent state-of-the-art tracking systems in term of accuracy and robustness.

@inproceedings{Noor2017:AIPR_KCLoFT,
  author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Vehicle tracking in wide area motion imagery using KC-LoFT multi-feature discriminative modeling",
  year = 2017,
  booktitle = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-6",
  keywords = "tracking, wide area motion imagery, kernelized correlation filter, discriminative modeling, ridge regression",
  doi = "10.1109/AIPR.2017.8457953"
}

N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Vehicle tracking in wide area motion imagery using KC-LoFT multi-feature discriminative modeling. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, 2017.